| 摘要: |
| 针对股价数据具有高噪声、非线性和非平稳性等特征,使得股价精确预测非常困难的问题,提出小波-长短 记忆网络(LSTM-Wavelet)模型应用于股价预测。 首先,利用小波(Wavelet)分解降低金融时间序列的不稳定性,并 分析小波系数的细节特征;接着,发挥长短记忆网络(LSTM)模型的优势,深层挖掘小波系数中的长期依赖关系,对 分解后的各层小波系数分别建模预测;最后进行预测小波系数的数据重构。 使用中石油近两年的股价数据进行实 证分析,以每个交易日的开盘价、最高价、最低价、交易量为特征输入,预测当日中石油的收盘价。 结果表明:相较 于标准 LSTM 模型和小波- ARIMA (ARIMA-Wavelet)模型,提出的 LSTM-Wavelet 模型有更好的预测效果; 通过 小波分析将复杂股票数据,分解为长短记忆网络(LSTM)容易识别的小波系数,根据各层小波系数不同的数据特征 进行分层预测,提高了预测精度。 |
| 关键词: 股价预测 小波分解 LSTM 模型 LSTM-Wavelet 模型 |
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| Stock Price Prediction with LSTM-Wavelet Model Based on Deep Learning and Wavelet Analysis |
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LI Meng, HUANG Zhangjie, XU Jianhui
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School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China
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| Abstract: |
| Accurate stock price prediction is very difficult due to the high noise, non-linearity, and non-smoothness of stock price data. A wavelet-long and short-term memory network (LSTM-Wavelet) model was proposed for stock price prediction. First, wavelet decomposition was used to reduce the instability of financial time series, and the detailed features of wavelet coefficients were analyzed. Then, taking advantage of the long and short-term memory network (LSTM) model, the long-term dependencies in the wavelet coefficients were mined deeply and the predictions were modeled separately for each layer of the decomposed wavelet coefficients. Finally, the data reconstruction of the predicted wavelet coefficients was performed. Based on the empirical analysis of PetroChina’ s stock price data for the past two years, the opening price, high price, low price, and trading volume of each trading day were used as the characteristic inputs to predict the closing price of PetroChina on that day. The results showed that the proposed LSTM-Wavelet model had better prediction results compared with the standard LSTM model and the wavelet-ARIMA (ARIMA-Wavelet) model; the complex stock data were decomposed into wavelet coefficients easily recognized by Long Short Term Memory (LSTM) network through wavelet analysis, the stratified prediction was performed based on the data characteristics with different wavelet coefficients in each layer, and the prediction accuracy has been improved. |
| Key words: stock price prediction wavelet decomposition LSTM model LSTM-Wavelet model |
